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Record W2379768478 · doi:10.5539/jas.v8n6p137

Empirical Study on Effect of Silicon Application on Rice Blast Disease and Plant Morphology in Indonesia

2016· article· en· W2379768478 on OpenAlexvenueno aff
Adha Fatmah Siregar, Husnain Husnain, Kuniaki Sato, Toshiyuki Wakatsuki, Tsugiyuki Masunaga

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsBlast diseaseRandomized block designSiliconPenetration (warfare)HorticultureLimitingFertilizerYield (engineering)BiologyMaterials scienceChemistryAgronomyMathematicsMetallurgyBiochemistry

Abstract

fetched live from OpenAlex

<p>Si fertilizer was never used in rice cultivation by farmers in Indonesia. To evaluate the effect of Si application on blast disease, plant morphologies, and stomata formation on rice plant, a field experiment was conducted in West Java, Indonesia. Two treatments, Si+ (with 1000 kg ha<sup>-1 </sup>of silica gel) and Si- (without Si application) were set in a randomized complete block design. The results showed that Si application in soil with high available Si 426 mg SiO<sub>2</sub> kg<sup>-1</sup> significantly reduce leaf (p < 0.01) and neck (p < 0.05) blast disease infection and increased stomata density (p < 0.01). Si- had severer leaf blast infection than Si+ which could reach up to score 4 and 5. Si deposited on the tissue surface acts as a physical barrier by thickening the Si layer in cuticle which could decrease the number of blast lessions on leaf blades by limiting hypa penetration and invasion. Recently there was no report to prove whether Si deposition improves or changes the stomata density. The results confirmed that Si application have the potential of improving rice growth and yield through the increase of resistance to blast infection and increment in stomata density although they did not result in the yield increment in the present study.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.260
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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